DOI:https://doi.org/10.1007/s40565-018-0471-8 |
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Wind power prediction based on variational mode decomposition multi-frequency combinations |
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Author:
Gang ZHANG1, Hongchi LIU1, Jiangbin ZHANG1,
Ye YAN1, Lei ZHANG1, Chen WU1, Xia HUA2, Yongqing WANG3
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Author Affiliation:
1. Institute of Water Resources and Hydro-Electric Engineering,
Xi’an University of Technology, Xi’an 710048, China
2. State Grid Gansu Electric Power Company, Gansu Electric
Power Research Institute, Lanzhou 730050, China
3. State Grid Shaanxi Electric Power Company, Shaanxi
Electric Power Research Institute, Xi’an 710054, China
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Foundation: |
This work was supported by the National Natural
Science Foundation of China (No. 51507141), the National Key
Research and Development Program of China (No.
2016YFC0401409) and the Shaanxi provincial education office fund
(No. 17JK0547). |
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Abstract: |
Because of the uncertainty and randomness of
wind speed, wind power has characteristics such as nonlinearity
and multiple frequencies. Accurate prediction of
wind power is one effective means of improving wind
power integration. Because the traditional single model
cannot fully characterize the fluctuating characteristics of
wind power, scholars have attempted to build other prediction
models based on empirical mode decomposition
(EMD) or ensemble empirical mode decomposition
(EEMD) to tackle this problem. However, the prediction
accuracy of these models is affected by modal aliasing and
illusive components. Aimed at these defects, this paper
proposes a multi-frequency combination prediction model
based on variational mode decomposition (VMD). We use
a back propagation neural network (BPNN), autoregressive
moving average (ARMA) model, and least squares support
vector machine (LS-SVM) to predict high, intermediate,
and low frequency components, respectively. Based on the
predicted values of each component, the BPNN is applied
to combine them into a final wind power prediction value.
Finally, the prediction performance of the single prediction
models (ARMA, BPNN, LS-SVM) and the decomposition
prediction models (EMD and EEMD) are used to compare
with the proposed VMD model according to the evaluation
indices such as average absolute error, mean square error,
and root mean square error to validate its feasibility and
accuracy. The results show that the prediction accuracy of
the proposed VMD model is higher. |
Keywords: |
Wind power prediction, Variational mode
decomposition, Multi-frequency combination prediction,
Back propagation neural network, Autoregressive moving
average model, Least square support vector machine |
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Online Time:2019/03/08 |
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